Oscar Dalmau

Cervical cancer is the fourth most frequently diagnosed cancer among women worldwide, and it is a leading cause of death in women. The diagnosis is performed manually by analyzing the cells in Pap smear tests, this causes diagnoses to be delayed and results in a high rate of false positives. To address this issue, a Multilayer Perceptron (MLP) artificial neural network model is proposed, utilizing local features to automatically classify squamous cell nuclei. For model training, two public datasets were used: CRIC and SIPaKMeD. Unlike many of the reported methods of the state of the art, our proposal learns from the content of whole images, achieving an accuracy of 0.9526 and 0.9397 on the CRIC and Sipakmed datasets, respectively. Our proposal provides a simple approach while maintaining performance comparable to that of complex models reported in the literature.

Magnetic resonance images are usually corrupted by noise during the acquisition process, which can affect the results of subsequent medical image analysis and diagnosis. This paper presents a denoising recurrent convolutional neural network for Brain MRI denoising. The proposed model consists of a one-level autoencoder architecture with a shortcut, in which the standard convolutional blocks are changed for a new recurrent convolutional denoising block. This block is based on the gated recurrent units combined with local residual learning, allowing us to filter the noisy image recursively. Additionally, we adopt global residual learning to directly estimate the corrupted image’s noise instead of the noise-free image. The proposed model requires less computation than other models based on neural networks and experimentally outperforms state-of-the-art models on clinical brain MRI datasets, particularly for high noise levels.

In recent years, the performance of convolutional neural networks in single-image super-resolution has improved significantly. However, most state-of-the-art models address the super-resolution problem for specific scale factors. In this paper, we propose a convolutional neural network for arbitrary scale super-resolution. Specifically, given a range of scale factors, the proposed model can generate super-resolution images with non-integer scale factors within the range. The proposed model incorporates a channel-spatial attention block in which the scale factor is also provided. This module recovers the most relevant information from the low-resolution image given the scale factor and enhances the upsampled image before generating the high-resolution target image. This channel attention block allows learning the channel and spatial dependencies. Additionally, we incorporate global residual learning so that the model recovers the details of an upsampled low-resolution image by interpolation. We evaluated the proposed method through extensive experiments on widely used benchmark datasets for single-image super-resolution. In order to assess the performance of the model, we used the peak signal-to-noise ratio and the structural similarity index measure. The proposed model achieves an average of 35.36, 31.78, 29.62 for peak signal-to-noise ratio, and 0.9410, 0.8828, 0.8334 for structural similarity index measure for the standard evaluation scale factors $\times 2$ , $\times 3$ , $\times 4$ , respectively. The experimental results show a better performance of the proposed model over other state-of-the-art models for arbitrary scale super-resolution, and are competitive with models trained for specific scale factors.